Air conditioner energy-saving control cabinet with carbon emission metering function and control method

Abstract

The application discloses an air conditioner energy-saving control cabinet with carbon emission calculation function and a control method, and belongs to the technical field of building energy saving. The control cabinet comprises a cabinet body and a cabinet door, an industrial computer host and a protocol gateway are arranged in the cabinet body, and an IO control board is arranged on the cabinet door. The industrial computer host is embedded with an energy-saving control algorithm module and a carbon emission calculation model, the protocol gateway is used for collecting energy data, and the IO control board is used for controlling air conditioning equipment. Through hardware integration and software algorithm, the application realizes real-time monitoring, measurement and collaborative optimization control of the energy consumption and carbon emission of an air conditioning system, solves the problems of energy and carbon management separation and the inability to realize closed-loop linkage in a traditional scheme, and is particularly suitable for scenes such as commercial buildings and data centers which have high requirements on energy saving and carbon reduction.

Description

Technical Field

[0001] This invention relates to the field of building energy conservation technology, specifically to an air conditioning energy-saving control cabinet and control method with carbon emission metering function. Background Technology

[0002] In high-energy-consuming scenarios such as commercial buildings and data centers, air conditioning systems are among the main energy-consuming devices, and optimizing their energy-saving operation has always been a key research focus in this field. Meanwhile, with increasing environmental management requirements, the need for accurate measurement and management of carbon emissions related to energy use is also growing.

[0003] Currently, relevant technical solutions on the market are mainly divided into two categories: one is a control system focused on the energy-saving operation of air conditioning equipment, such as the "Central Air Conditioning Energy-Saving Control Cabinet" disclosed in CN206572713U. It reduces energy consumption by optimizing start-up and shutdown, load adjustment, and other strategies. However, this type of system has limited functionality and lacks the ability to directly measure and control carbon emissions. The other category is an independent building energy management system or carbon emission monitoring platform, such as the "An Internet of Things-Based Intelligent Building Energy Consumption Monitoring Method and System" disclosed in CN202311463515.0. It mainly realizes data collection, remote transmission, and visualization. It usually exists as an upper-level management platform and is difficult to form a deep closed-loop linkage with the real-time control logic of the underlying air conditioning equipment.

[0004] Existing technologies suffer from the following significant drawbacks: Limited functionality and fragmented systems: Energy-saving control and carbon emission metering are implemented by different systems, resulting in redundant hardware configurations and inefficient data flow, making it impossible to dynamically control the air conditioning system based on real-time carbon emission data. Poor real-time control and management: Independent carbon emission monitoring systems often focus on data reporting and post-event analysis, with significant delays in data feedback to the control end, failing to meet the requirements for real-time optimized control. Lack of collaborative optimization strategies: Existing air conditioning energy-saving strategies typically only consider energy consumption or economic costs, failing to incorporate carbon emission intensity as a key optimization objective, thus failing to achieve synergistic minimization of energy consumption and carbon emissions.

[0005] Therefore, there is an urgent need in this field for a highly integrated solution that deeply integrates the real-time carbon emission metering function with the intelligent control function of the air conditioning system to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide an air conditioning energy-saving control cabinet and control method with carbon emission metering function, so as to solve the problem that energy consumption control and carbon emission management are separated in the prior art and cannot achieve synergistic optimization.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An air conditioning energy-saving control cabinet with carbon emission metering function includes a cabinet body and a cabinet door. The cabinet body is equipped with: The industrial control host has an embedded energy-saving control algorithm module and a carbon emission metering calculation model. The carbon emission metering calculation model has a built-in carbon emission factor database. Protocol gateway, connected to the industrial control host, is used to collect data from energy metering instruments in the air conditioning system; The cabinet door has: The IO control board connects to the industrial control host and is used to receive control signals and sensor signals from the air conditioning system.

[0008] Furthermore, a touch screen is also provided on the cabinet door for parameter setting, status display, and report viewing.

[0009] Furthermore, the cabinet is equipped with a DC switching power supply to provide isolated power to the industrial control host, IO control board and protocol gateway.

[0010] Furthermore, the cabinet is equipped with an industrial Ethernet switch to enable data exchange between the industrial control host and the external network.

[0011] A control method for an air conditioning energy-saving control cabinet with carbon emission metering function, wherein the control method is executed by an industrial control host, includes the following steps: S1. The carbon emission metering calculation model collects multi-source energy consumption data of the air conditioning system through the protocol gateway and combines it with the carbon emission factor database to calculate the carbon emission intensity of the air conditioning system in real time. S2, the energy-saving control algorithm module obtains the target operating mode of the air conditioning system through an optimization function based on the real-time electricity price, the real-time grid carbon emission intensity factor and the calculated carbon emission intensity, and sends it to the actuator of the air conditioning system through the IO control board; S3, the energy-saving control algorithm module monitors the execution results of the actuator and provides real-time feedback to optimize and adjust the control parameters.

[0012] Further, S1 includes: S11, collecting the energy consumption corresponding to various energy metering instruments in the air conditioning system through the protocol gateway; S12, calculating the carbon emissions of each energy source based on the energy consumption and the carbon emission factors corresponding to various energy sources in the carbon emission factor database, using the following formula: i Carbon emissions of one energy source = the first energy source i Energy consumption × the first i S13. Sum the carbon emission factors of all energy sources to obtain the carbon emission of the air conditioning system; S14. Calculate the carbon emission intensity of the air conditioning system based on the carbon emission of the air conditioning system and the real-time measured cooling output of the air conditioning system. The calculation formula is: Carbon emission intensity of air conditioning system = Carbon emission of air conditioning system / Cooling output of air conditioning system.

[0013] Furthermore, in S2, the optimization function is defined as the weighted sum of energy cost, carbon emission cost, and comfort cost, and its expression is: J=min[W1·EC+W2·CbC+W3·CfC]; Where J is the optimization function; W1, W2, and W3 are configurable weight coefficients; EC is the energy cost; CbC is the carbon emission cost; and CfC is the comfort cost. EC= P ( t )×EP( t )×Δ t )+PDC; CbC= P ( t )×GCF( t )×Δ t ); CfC= PMV ( t )−PMV set ) 2 ; Where P(t) is the value at time... t Total electrical power of the air conditioning system at that time; Δ t The time interval for calculation; EP( t ) for time t Real-time electricity price; PDC is the monthly maximum demand electricity charge; GCF ( t ) for time t Real-time grid carbon intensity factor; PMV ( t ) for time t The predicted average voter turnout in the room; PMV set Set the desired PMV value.

[0014] Furthermore, the optimization function is implemented through the following steps: S21. Map the real-time electricity price and the real-time grid carbon emission intensity factor to a unified scoring range to obtain the electricity price score and the carbon emission intensity score. S22. Based on the preset weighting coefficients, the electricity price score, carbon emission intensity score and the calculated carbon emission intensity are weighted and calculated to obtain a comprehensive urgency index. S23. Compare the comprehensive urgency index with the predefined decision table. The decision table defines the mapping relationship between different index ranges and temperature compensation values. Query the corresponding temperature compensation values. S24. Convert the temperature compensation value into a temperature setpoint compensation command and send it to the actuator of the air conditioning system through the IO control board to adjust the operating setpoint of the air conditioning system.

[0015] Furthermore, in S3, the real-time feedback optimization adjustment of control parameters is as follows: S31, obtain the real-time grid carbon emission intensity factor, real-time electricity price, indoor temperature and the operating status of the air conditioning system; S32, within a preset period, obtain the real-time temperature compensation value through the optimization function; S33, add the real-time temperature compensation value to the basic set value to generate a new operating set value, and send it to the air conditioning system for execution.

[0016] Furthermore, in S2, when both the real-time electricity price signal and the real-time grid carbon emission intensity signal are higher than their respective preset thresholds, the optimal operating mode is determined to be the load reduction, energy saving, and carbon reduction mode. The load reduction, energy saving, and carbon reduction mode is achieved by applying a positive energy-saving temperature compensation value to the set temperature of the chilled water supply to the air conditioning system, wherein the absolute value of the energy-saving temperature compensation value is constrained to the range of 1.5℃.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention integrates core carbon emission metering hardware and energy-saving control software into a standard control cabinet, breaking down the barriers between energy and carbon management and achieving integrated data acquisition, metering, and control. Specifically, through the embedded algorithm module, carbon emission intensity is used as the control target and linked with electricity price signals and grid carbon emission intensity signals to achieve synchronous and real-time optimization of air conditioning system energy consumption and carbon emissions. Attached Figure Description

[0018] Figure 1 This is an external view of the control cabinet of the present invention.

[0019] Figure 2 This is a layout diagram of the control cabinet of the present invention.

[0020] Figure 3 This is a flowchart of the control method of the present invention.

[0021] The names corresponding to the reference numerals in the attached figures are as follows: 1-Cabinet door; 2-Touch screen; 3-Door lock; 4-IO control board; 5-Assembly board; 6-Double-pole air switch; 7-DC switching power supply; 8-Industrial Ethernet switch; 9-Cable hole; 10-Terminal block; 11-Protocol gateway; 12-Industrial control host; 13-Cabinet body. Detailed Implementation

[0022] Terminology Explanation: PID is an abbreviation for Proportional, Integral, and Derivative, a type of control algorithm.

[0023] ESG is an acronym for Environment, Social, and Governance, a comprehensive framework used to measure the sustainability performance and long-term investment value of businesses and organizations.

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0026] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; of course, they can also refer to a mechanical connection or an electrical connection; furthermore, they can refer to a direct connection, an indirect connection through an intermediate medium, or a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] like Figures 1-2As shown, this invention provides an air conditioning energy-saving control cabinet with carbon emission metering function, using a standard industrial cabinet, including a cabinet body 13 and a cabinet door 1. The cabinet body 13 has an assembly plate 5 inside, on which components such as an industrial control host 12, a protocol gateway 11, an industrial Ethernet switch 8, a DC switching power supply 7, and a double-pole air switch 6 are installed from top to bottom or in sections. Cables are introduced through the bottom through-hole 9 and connected to the corresponding terminal blocks 10. The industrial control host 12 has an embedded energy-saving control algorithm module (PID + fuzzy adaptive control) and a carbon emission metering calculation model. The carbon emission metering calculation model has a built-in carbon emission factor database, which conforms to IPCC guidelines and national standards and can be updated in real time via the cloud. The protocol gateway 11, connected to the industrial control host 12, is used to collect data from energy metering instruments in the air conditioning system and supports industrial protocols such as Modbus RTU / TCP / IP. The cabinet door 1 has a touch screen 2 and an IO control board 4, which is connected to the industrial control host 12 and used to access control signals and sensor signals from the air conditioning system. The cabinet door 1 is also equipped with a door lock 3.

[0028] In practical applications, the protocol gateway 11 connects to data from energy metering instruments in the air conditioning system, such as smart meters and water meters, via interfaces like RS485. The IO control board 4 communicates with the industrial control host 12 via protocols such as TCP / IP, providing a rich set of DI / DO / AI / AO standard signal interfaces. It also connects to controlled equipment in the air conditioning system, such as chillers, water pumps, and fan coil units, as well as temperature and humidity sensors, via control cables, and receives sensor signals such as temperature and pressure. The industrial control host 12 connects to the local area network via the industrial Ethernet switch 8, and can obtain signals such as external electricity prices and carbon emission intensity.

[0029] like Figure 3 As shown, the present invention provides a control method for an air conditioning energy-saving control cabinet with carbon emission metering function, executed by an industrial control host, including the following steps: S1. The carbon emission metering calculation model collects multi-source energy consumption data of the air conditioning system through the protocol gateway and combines it with the carbon emission factor database to calculate the carbon emission intensity of the air conditioning system in real time. S2, the energy-saving control algorithm module obtains the target operating mode of the air conditioning system through an optimization function based on the real-time electricity price, the real-time grid carbon emission intensity factor and the calculated carbon emission intensity, and sends it to the actuator of the air conditioning system through the IO control board; S3, the energy-saving control algorithm module monitors the execution results of the actuator and provides real-time feedback to optimize and adjust the control parameters.

[0030] This invention achieves real-time monitoring, metering, and collaborative optimization control of energy consumption and carbon emissions of air conditioning systems through hardware integration and software algorithms. It solves the problems of energy and carbon management separation and inability to achieve closed-loop linkage in traditional solutions, and is particularly suitable for scenarios with high requirements for energy conservation and carbon reduction, such as commercial buildings and data centers.

[0031] In some embodiments of the present invention, S1 includes: S11, collecting the energy consumption corresponding to various energy metering instruments in the air conditioning system through a protocol gateway; S12, calculating the carbon emissions of each energy source based on the energy consumption and the carbon emission factors corresponding to various energy sources in the carbon emission factor database, using the following formula: i Carbon emissions of one energy source = the first energy source i Energy consumption × the first i S13. Sum the carbon emission factors of all energy sources to obtain the carbon emission of the air conditioning system; S14. Calculate the carbon emission intensity of the air conditioning system based on the carbon emission of the air conditioning system and the real-time measured cooling output of the air conditioning system. The calculation formula is: Carbon emission intensity of air conditioning system = Carbon emission of air conditioning system / Cooling output of air conditioning system.

[0032] S11 specifically refers to the protocol gateway periodically (e.g., every 15 seconds or every minute) reading real-time energy consumption data, such as electricity consumption (kWh), gas consumption (m³), and water consumption (tons), from various smart metering instruments (including but not limited to smart electricity meters, water meters, gas meters, and heating / cooling meters) connected to the air conditioning system and the main entrance of the building, through its supported industry standard protocols (such as Modbus RTU / TCP, BACnet, etc.).

[0033] Prior to S12, the industrial control host preprocesses and transforms the acquired real-time energy consumption data. Preprocessing includes verification and filtering of outliers. Additionally, since real-time energy consumption data is mostly instantaneous flow, the instantaneous flow data needs to be integrated and converted into a cumulative energy consumption value for a specific time window (e.g., per hour).

[0034] S12 specifically refers to: The carbon emission measurement calculation model uses the following formula: i Carbon emissions of one energy source = the first energy source i Energy consumption × the first i The carbon emission factors for various energy sources are used to obtain the carbon emissions of each energy source. The carbon emission factor database is built into the industrial control host's memory and pre-stores default carbon emission factors for various energy sources from authoritative sources such as the *IPCC National Greenhouse Gas Inventory Guidelines* and the *Provincial Greenhouse Gas Inventory Compilation Guidelines* (e.g., the average emission factor for the power grid is X kgCO2e / kWh, and the emission factor for natural gas is Y kgCO2e / m³). 3The industrial control host can also access the Internet via an industrial Ethernet switch to obtain updated, regionalized carbon emission factors from an authorized data service platform in real time or periodically. For example, it can obtain the marginal or average emission factors of the current regional power grid every hour, thereby more accurately reflecting the real-time carbon emissions generated by electricity consumption.

[0035] Then, the carbon emissions of all energy types are summed to obtain the total carbon emissions of the system during that time period. Finally, based on the carbon emissions of the air conditioning system and the real-time measured cooling output of the air conditioning system, the carbon emission intensity of the air conditioning system is calculated. All data in S1, including the consumption and carbon emissions of each energy source, the carbon emissions of the air conditioning system, and the carbon emission intensity, are stored in real-time in the database of the industrial control host. The data is dynamically displayed in real-time on the touchscreen on the cabinet door, and trend curves can be generated, such as carbon emission curves and carbon emission intensity curves. The carbon emission curve shows the trend of total carbon emissions over time (e.g., 24 hours), and the carbon emission intensity curve shows the trend of carbon emission intensity over time.

[0036] The trend curves generated from visualized and in-depth analysis of carbon emission data form the intelligent decision-making hub driving carbon management from perception to action. It achieves closed-loop management throughout the entire process, from diagnosis and source tracing to forward-looking planning, and is a core tool for precise carbon emission control and continuous emission reduction. Its specific application scenarios cover the following five levels: 1. Precise Diagnosis and Source Tracing: By analyzing abnormal peaks in the curve and correlated with equipment operation logs and production schedules in time and space, high-carbon emission sources are accurately located, providing precise data entry points for energy and carbon optimization. 2. Performance Benchmark Management: A carbon emission intensity benchmark is established based on historical data, and scientific and traceable emission reduction targets are set accordingly. By comparing real-time curves with the benchmark and target lines, quantitative management and visual evaluation of carbon emission performance are achieved, ensuring the effective implementation of emission reduction strategies. 3. Optimized Control Strategies: By combining real-time carbon emission curves with dynamic grid carbon emission factors, low-carbon periods in the grid are identified. Based on this, flexible loads such as central air conditioning and energy storage systems are automatically scheduled to operate preferentially during low-carbon periods. 4. Predictive and Forward-Looking Decision-Making: Utilizing machine learning and other algorithms, based on historical carbon emission curves, meteorological data, and production planning data, the system predicts future carbon emission trends. Based on this, the system can generate forward-looking energy dispatching solutions (such as advance cooling), achieving an optimal solution between cost and carbon emissions. 5. Compliance Disclosure and Communication: Automatically generates carbon emission curves and analysis reports that comply with domestic and international standards, serving internal management reporting and external ESG information disclosure.

[0037] In some embodiments of the present invention, the optimization function is defined as the weighted sum of energy cost, carbon emission cost and comfort cost, and the expression is: J=min[W1·EC+W2·CbC+W3·CfC]; Where J is the optimization function; W1, W2, and W3 are configurable weight coefficients; EC is the energy cost; CbC is the carbon emission cost; and CfC is the comfort cost. EC= P ( t )×EP( t )×Δ t )+PDC; CbC= P ( t )×GCF( t )×Δ t ); CfC= PMV ( t )−PMV set ) 2 ; Where P(t) is the value at time... t Total electrical power of the air conditioning system at that time; Δ t The time interval for calculation; EP( t ) for time t Real-time electricity price; PDC is the monthly maximum demand electricity charge; GCF ( t ) for time t Real-time grid carbon intensity factor; PMV ( t ) for time t The predicted average voter turnout in the room; PMV set Set the desired PMV value.

[0038] Specifically, the real-time grid carbon emission intensity factor is obtained by accessing official data interfaces or parsing open data services released by authoritative institutions; official data sources include data regularly released by institutions such as the State Grid Corporation of China and the Ministry of Ecology and Environment, reflecting the real-time or hourly marginal / average carbon emission intensity of the power grid.

[0039] Specifically, in air conditioning energy-saving control cabinets, the Predicted Average Voting Value (PMV) can also be used as core data for quantifying human thermal comfort. The implementation method includes the following steps: 1.1. Data Sensing: The industrial control host collects six environmental and personnel parameters required for PMV calculation in real time through the BMS standard communication interface: Environmental parameters: dry-bulb temperature (Ta), mean radiant temperature (Tr), air velocity (Va), and relative humidity (RH); Personnel parameters: human activity level (Met) and clothing thermal resistance (Clo) preset based on the service area's business type. 1.2. Real-time Calculation: The PMV calculation model embedded in the industrial control host is periodically called. This PMV calculation model is a known model based on the ISO7730 standard configuration. The collected real-time parameters are substituted into the model to dynamically calculate the current PMV value, serving as a precise quantitative indicator of comfort. 1.3. Feedback Control: The calculated real-time PMV value is compared with the comfort range [-0.5, +0.5], and corresponding comfort adjustment instructions are generated to form a closed-loop control: if PMV > +0.5 (too hot), the air conditioning system is instructed to reduce the supply air temperature or increase the air volume; if PMV < -0.5 (too cold), the air conditioning system is instructed to increase the supply air temperature or decrease the air volume.

[0040] The optimization function, through a multi-objective weighted function and dynamic factors (electricity price, grid carbon emission intensity factor), combined with a predictive average vote (PMV) comfort model, achieves the optimal dynamic balance between economy, environmental protection, and human comfort in the air conditioning system while satisfying the hard constraints of system operation. Furthermore, the system's strategy preferences can be flexibly defined by adjusting the weighting coefficients W1, W2, and W3. For example, W2 can be increased when carbon quotas are strict; W1 can be increased when extreme cost-saving is required; and W3 can be increased in areas with extremely high comfort requirements.

[0041] The optimization function is implemented through the following steps: S21. Map the real-time electricity price and the real-time grid carbon emission intensity factor to a unified scoring range to obtain the electricity price score and the carbon emission intensity score. S22. Based on the preset weighting coefficients, the electricity price score, carbon emission intensity score and the calculated carbon emission intensity are weighted and calculated to obtain a comprehensive urgency index. S23. Compare the comprehensive urgency index with the predefined decision table. The decision table defines the mapping relationship between different index ranges and temperature compensation values. Query the corresponding temperature compensation values. S24. Convert the temperature compensation value into a temperature setpoint compensation command and send it to the actuator of the air conditioning system through the IO control board to adjust the operating setpoint of the air conditioning system.

[0042] The optimization function requires setting key system parameters, such as: Basic comfort temperature: 24°C (summer cooling mode); Permissible ΔT range: 0°C to +2.0°C (the setting can only be increased in summer and decreased in winter to save energy); Carbon emission intensity range: 300gCO2 / kWh (minimum) to 800gCO2 / kWh (maximum); Electricity price range: 0.4 yuan / kWh (minimum) to 1.2 yuan / kWh (maximum); Weighting coefficients: α=0.4, β=0.3, γ=0.3.

[0043] S21 involves mapping the real-time electricity price and the real-time grid carbon emission intensity factor to a unified scoring range using the normalization formula: score = 10 × (real-time value - minimum value) / (maximum value - minimum value), thus obtaining the electricity price score and carbon emission intensity score. For example, if the obtained real-time electricity price P_now = 1.0 yuan / kWh, the real-time grid carbon emission intensity factor C_now = 650gCO2 / kWh, the electricity price score S_price = 10 × (1.0 - 0.4) / (1.2 - 0.4) = 7.5, and S_gridcarbon = 10 × (650 - 300) / (800 - 300) = 7.0.

[0044] S22 involves a weighted calculation of the electricity price score, carbon emission intensity score, and calculated carbon emission intensity to obtain a comprehensive urgency index. The weighted calculation is as follows: U = α × S_gridcarbon + β × S_price + γ × S_systemcarbon, where S_systemcarbon is the calculated carbon emission intensity, for example, S_systemcarbon = 6.5. U = 0.4 × 7.0 + 0.3 × 7.5 + 0.3 × 6.5 = 7.

[0045] In step S23, a decision table is predefined: U<4.0 indicates that comfort is prioritized, carbon emissions and electricity price pressures are low, and optimal comfort is maintained; 4.0≤U<6.0, slight energy saving; ΔT: +0.5, -0.5, indicating initial pressure, allowing for small, imperceptible adjustments. 6.0≤U<8.0, moderate energy consumption reduction, ΔT: +1.0, -1.0, indicating relatively high pressure, which can be adjusted significantly but within an acceptable range; U≥8.0 indicates a strong response; ΔT: +1.5, -1.5 indicates significant pressure, which can allow for substantial adjustments to protect the power grid and carbon targets.

[0046] The overall urgency index obtained from S22 is U=7, with corresponding ΔT values ​​of +1.0 and -1.0. Since it is summer, the set value can only be increased to save energy, so ΔT=+1.0.

[0047] S23 converts the compensation value into an instruction that the air conditioning system can execute, and obtains the actual operating setpoint of the air conditioning system = basic comfort temperature + ΔT = 24°C + 1.0°C = 25.0°C.

[0048] Specifically, the optimization function needs to be optimized under the following constraints: Equipment capacity constraints: T_supply_min≤T_supply≤T_supply_max (upper and lower limits of chilled water supply temperature).

[0049] Comfort constraints: T_room_min≤T_room≤T_room_max or PMV_min≤PMV≤PMV_max.

[0050] Logical constraints: such as the minimum interval between device start-up and shutdown.

[0051] Equipment capability constraints prevent optimization algorithms from sacrificing carbon reduction for extreme energy savings. They calculate chilled water temperatures that a host device simply cannot achieve (e.g., -5℃) or that are ineffective (e.g., 15℃), thus protecting equipment safety and ensuring basic system functionality. Comfort constraints are crucial for achieving seamless energy saving and carbon reduction. They ensure that when the algorithm adjusts the water temperature (ΔT), it does not cause noticeable discomfort in the indoor environment, guaranteeing the practicality of the technology and user experience. Logical constraints consider the physical inertia and lifespan protection of the equipment, avoiding short-cycle operations for minimal energy savings, reflecting the long-term reliability of the system.

[0052] In some embodiments of this invention, when both the real-time electricity price and the real-time grid carbon emission intensity factor are higher than their respective preset thresholds, the optimal operating mode is determined to be the load reduction, energy saving, and carbon reduction mode. The load reduction, energy saving, and carbon reduction mode is a standardized response plan for the system when facing maximum cost and economic pressure. This mode is achieved by applying a positive energy-saving temperature compensation value to the set temperature of the chilled water supply to the air conditioning system, wherein the absolute value of the energy-saving temperature compensation value is constrained to within 1.5°C. This mode achieves the instantaneous synergistic maximization of the dual objectives of "energy saving" and "carbon reduction" in a way that minimizes the impact on users during the period of worst external environmental (economic and environmental) conditions.

[0053] In some embodiments of the present invention, the feedback optimization of S4 specifically includes: S41, acquiring the grid carbon emission intensity information factor, real-time electricity price, indoor temperature and the operating status of the air conditioning system in real time; within a preset period, calculating the real-time temperature compensation value based on the real-time carbon emission intensity factor and the real-time electricity price through an optimization function; S43, adding the real-time temperature compensation value to the basic setting value to generate a new operating setting value, and sending it to the air conditioning system for execution.

[0054] The S4 also includes: displaying temperature setting strategies, including new operating settings, on the touchscreen; and a temporary manual veto function, allowing users to temporarily suspend system optimization decisions and automatically resume after a preset time. Human-machine interaction via the touchscreen, with transparent strategy prompts and the granting of temporary veto rights, greatly enhances system reliability, user experience, and market acceptance while ensuring the efficiency of automated control.

[0055] In some embodiments of the present invention, the control method further includes predicting the future hourly load of the air conditioning system based on outdoor weather forecast data and building occupancy rate data; and dynamically generating and executing an operation plan that includes a pre-cooling stage and a flexible peak-shaving stage, in conjunction with future electricity price signals and grid carbon emission intensity signals; wherein, in the pre-cooling stage: the set temperature of the chilled water in the air conditioning system is reduced in advance when both the electricity price and grid carbon emission intensity signals are low; and in the flexible peak-shaving stage, the set temperature of the chilled water in the air conditioning system is increased when both the electricity price and carbon emission intensity are high.

[0056] The industrial control host 12, IO control board 4, protocol gateway 11, touch screen 2, DC switching power supply 7, and industrial Ethernet switch 8 used in this invention are all existing known electrical devices, and all can be purchased and used directly on the market. Their structure, circuit, and control principle are all existing known technologies. Therefore, the structure, circuit, and control principle of the industrial control host 12, IO control board 4, protocol gateway 11, touch screen 2, DC switching power supply 7, and industrial Ethernet switch 8 will not be described in detail here.

[0057] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention used to illustrate the technical solutions of the present invention, and are not intended to limit the invention, nor are they intended to limit the patent scope of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. That is to say, any changes or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but whose technical problems are still consistent with the present invention, should be included within the protection scope of the present invention. In addition, the direct or indirect application of the technical solutions of the present invention to other related technical fields are similarly included within the patent protection scope of the present invention.

Claims

1. An air conditioning energy-saving control cabinet with carbon emission metering function, comprising a cabinet body (13) and a cabinet door (1), characterized in that, The cabinet (13) has the following features inside: The industrial control host (12) has an embedded energy-saving control algorithm module and a carbon emission metering calculation model. The carbon emission metering calculation model has a built-in carbon emission factor database. Protocol gateway (11) is connected to industrial control host (12) and is used to collect data from energy metering instruments in air conditioning system; The cabinet door (1) is equipped with: The IO control board (4) is connected to the industrial control host (12) and is used to access the control signals and sensor signals of the air conditioning system.

2. An air conditioning energy-saving control cabinet with carbon emission metering function according to claim 1, characterized in that, The cabinet door (1) is also equipped with a touch screen (2) for parameter setting, status display and report viewing.

3. An air conditioning energy-saving control cabinet with carbon emission metering function according to claim 1, characterized in that, The cabinet (13) is also equipped with a DC switching power supply (7) to provide isolated power supply for the industrial control host (12), IO control board (4) and protocol gateway (11).

4. An air conditioning energy-saving control cabinet with carbon emission metering function according to claim 1, characterized in that, The cabinet (13) is also equipped with an industrial Ethernet switch (8) to realize data exchange between the industrial control host (12) and the external network.

5. A control method for an air conditioning energy-saving control cabinet with carbon emission metering function according to any one of claims 1 to 4, characterized in that, The control method is executed by the industrial control host and includes the following steps: S1. The carbon emission metering calculation model collects multi-source energy consumption data of the air conditioning system through the protocol gateway and combines it with the carbon emission factor database to calculate the carbon emission intensity of the air conditioning system in real time. S2, the energy-saving control algorithm module obtains the target operating mode of the air conditioning system through an optimization function based on the real-time electricity price, the real-time grid carbon emission intensity factor and the calculated carbon emission intensity, and sends it to the actuator of the air conditioning system through the IO control board; S3, the energy-saving control algorithm module monitors the execution results of the actuator and provides real-time feedback to optimize and adjust the control parameters.

6. The control method for an air conditioning energy-saving control cabinet with carbon emission metering function according to claim 5, characterized in that, S1 includes: S11. Collect the energy consumption corresponding to various energy metering instruments in the air conditioning system through the protocol gateway; S12. Based on the consumption of each energy source and the corresponding carbon emission factors for each energy source in the carbon emission factor database, calculate the carbon emissions of each energy source. The calculation formula is: i Carbon emissions of one energy source = the first energy source i Energy consumption × the first i S13. Sum the carbon emission factors of all energy sources to obtain the carbon emission of the air conditioning system; S14. Calculate the carbon emission intensity of the air conditioning system based on the carbon emission of the air conditioning system and the real-time measured cooling output of the air conditioning system. The calculation formula is: Carbon emission intensity of air conditioning system = Carbon emission of air conditioning system / Cooling output of air conditioning system.

7. The control method for an air conditioning energy-saving control cabinet with carbon emission metering function according to claim 5, characterized in that, In S2, the optimization function is defined as the weighted sum of energy cost, carbon emission cost and comfort cost, and the expression is: J=min[W1·EC+W2·CbC+W3·CfC]; Where J is the optimization function; W1, W2, and W3 are configurable weight coefficients; EC is the energy cost; CbC is the carbon emission cost; and CfC is the comfort cost. EC= P ( t )×EP( t )×Δ t )+PDC; CbC= P ( t )×GCF( t )×Δ t ); CfC= PMV( t )−PMV set ) 2 ; Where P(t) is the value at time... t Total electrical power of the air conditioning system at that time; Δ t The time interval for calculation; EP( t ) for time t Real-time electricity price; PDC is the monthly maximum demand electricity charge; GCF ( t ) for time t Real-time grid carbon intensity factor; PMV ( t ) for time t The predicted average voter turnout in the room; PMV set Set the desired PMV value.

8. The control method for an air conditioning energy-saving control cabinet with carbon emission metering function according to claim 7, characterized in that, The optimization function is implemented through the following steps: S21. Map the real-time electricity price and the real-time grid carbon emission intensity factor to a unified scoring range to obtain the electricity price score and the carbon emission intensity score. S22. Based on the preset weighting coefficients, the electricity price score, carbon emission intensity score and the calculated carbon emission intensity are weighted and calculated to obtain a comprehensive urgency index. S23. Compare the comprehensive urgency index with the predefined decision table. The decision table defines the mapping relationship between different index ranges and temperature compensation values. Query the corresponding temperature compensation values. S24. Convert the temperature compensation value into a temperature setpoint compensation command and send it to the actuator of the air conditioning system through the IO control board to adjust the operating setpoint of the air conditioning system.

9. The control method for an air conditioning energy-saving control cabinet with carbon emission metering function according to claim 7, characterized in that, In S3, the real-time feedback optimization adjustment control parameters are as follows: S31, obtain the real-time grid carbon emission intensity factor, real-time electricity price, indoor temperature and air conditioning system operating status; S32, within the preset period, obtain the real-time temperature compensation value through the optimization function; S33, add the real-time temperature compensation value to the basic set value to generate a new operating set value, and send it to the air conditioning system for execution.

10. The control method for an air conditioning energy-saving control cabinet with carbon emission metering function according to claim 5, characterized in that, In S2, when both the real-time electricity price signal and the real-time grid carbon emission intensity signal are higher than their respective preset thresholds, the optimal operating mode is the load reduction, energy saving, and carbon reduction mode. The load reduction, energy saving, and carbon reduction mode is achieved by applying a positive energy-saving temperature compensation value to the set temperature of the chilled water supply to the air conditioning system, wherein the absolute value of the energy-saving temperature compensation value is constrained to the range of 1.5℃.

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